{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout\n",
    "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n",
    "\n",
    "[1] [Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012](https://arxiv.org/abs/1207.0580)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout forward pass\n",
    "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n",
    "\n",
    "Once you have done so, run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tests with p =  0.25\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  10.014059116977283\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.749784\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.4\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.977917658761159\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.600796\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.7\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.987811912159426\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.30074\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.25, 0.4, 0.7]:                    # 这里的p是保留节点激活的概率\n",
    "  out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "  out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "  print('Running tests with p = ', p)\n",
    "  print('Mean of input: ', x.mean())\n",
    "  print('Mean of train-time output: ', out.mean())\n",
    "  print('Mean of test-time output: ', out_test.mean())\n",
    "  print('Fraction of train-time output set to zero: ', (out == 0).mean())\n",
    "  print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout backward pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx relative error:  5.44560814873387e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dropout_param = {'mode': 'train', 'p': 0.2, 'seed': 123}\n",
    "out, cache = dropout_forward(x, dropout_param)\n",
    "dx = dropout_backward(dout, cache)\n",
    "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n",
    "\n",
    "# Error should be around e-10 or less\n",
    "print('dx relative error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "What happens if we do not divide the values being passed through inverse dropout by `p` in the dropout layer? Why does that happen?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "在测试的时候网络的输出会有dropout，导致输出是随机的，所以需要在预测时除以p或在训练时直接除p来使训练和预测的输出概率一致"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-connected nets with Dropout\n",
    "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specifically, if the constructor of the net receives a value that is not 1 for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with dropout =  1\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "\n",
      "Running check with dropout =  0.75\n",
      "Initial loss:  2.302371489704412\n",
      "W1 relative error: 1.90e-07\n",
      "W2 relative error: 4.76e-06\n",
      "W3 relative error: 2.60e-08\n",
      "b1 relative error: 4.73e-09\n",
      "b2 relative error: 1.82e-09\n",
      "b3 relative error: 1.70e-10\n",
      "\n",
      "Running check with dropout =  0.5\n",
      "Initial loss:  2.3042759220785896\n",
      "W1 relative error: 3.11e-07\n",
      "W2 relative error: 1.84e-08\n",
      "W3 relative error: 5.35e-08\n",
      "b1 relative error: 2.58e-08\n",
      "b2 relative error: 2.99e-09\n",
      "b3 relative error: 1.13e-10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [1, 0.75, 0.5]:\n",
    "  print('Running check with dropout = ', dropout)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            weight_scale=5e-2, dtype=np.float64,\n",
    "                            dropout=dropout, seed=123)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Relative errors should be around e-6 or less; Note that it's fine\n",
    "  # if for dropout=1 you have W2 error be on the order of e-5.\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization experiment\n",
    "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a keep probability of 0.25. We will then visualize the training and validation accuracies of the two networks over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "(Iteration 1 / 125) loss: 7.856643\n",
      "(Epoch 0 / 25) train acc: 0.260000; val_acc: 0.184000\n",
      "(Epoch 1 / 25) train acc: 0.416000; val_acc: 0.258000\n",
      "(Epoch 2 / 25) train acc: 0.482000; val_acc: 0.276000\n",
      "(Epoch 3 / 25) train acc: 0.532000; val_acc: 0.277000\n",
      "(Epoch 4 / 25) train acc: 0.600000; val_acc: 0.271000\n",
      "(Epoch 5 / 25) train acc: 0.708000; val_acc: 0.299000\n",
      "(Epoch 6 / 25) train acc: 0.722000; val_acc: 0.282000\n",
      "(Epoch 7 / 25) train acc: 0.832000; val_acc: 0.255000\n",
      "(Epoch 8 / 25) train acc: 0.878000; val_acc: 0.269000\n",
      "(Epoch 9 / 25) train acc: 0.902000; val_acc: 0.275000\n",
      "(Epoch 10 / 25) train acc: 0.890000; val_acc: 0.261000\n",
      "(Epoch 11 / 25) train acc: 0.930000; val_acc: 0.282000\n",
      "(Epoch 12 / 25) train acc: 0.958000; val_acc: 0.300000\n",
      "(Epoch 13 / 25) train acc: 0.964000; val_acc: 0.305000\n",
      "(Epoch 14 / 25) train acc: 0.962000; val_acc: 0.314000\n",
      "(Epoch 15 / 25) train acc: 0.964000; val_acc: 0.303000\n",
      "(Epoch 16 / 25) train acc: 0.984000; val_acc: 0.309000\n",
      "(Epoch 17 / 25) train acc: 0.972000; val_acc: 0.322000\n",
      "(Epoch 18 / 25) train acc: 0.992000; val_acc: 0.317000\n",
      "(Epoch 19 / 25) train acc: 0.986000; val_acc: 0.308000\n",
      "(Epoch 20 / 25) train acc: 0.988000; val_acc: 0.315000\n",
      "(Iteration 101 / 125) loss: 0.052092\n",
      "(Epoch 21 / 25) train acc: 0.994000; val_acc: 0.313000\n",
      "(Epoch 22 / 25) train acc: 0.982000; val_acc: 0.305000\n",
      "(Epoch 23 / 25) train acc: 0.994000; val_acc: 0.306000\n",
      "(Epoch 24 / 25) train acc: 0.994000; val_acc: 0.301000\n",
      "(Epoch 25 / 25) train acc: 0.994000; val_acc: 0.296000\n",
      "0.9\n",
      "(Iteration 1 / 125) loss: 10.861761\n",
      "(Epoch 0 / 25) train acc: 0.244000; val_acc: 0.178000\n",
      "(Epoch 1 / 25) train acc: 0.370000; val_acc: 0.213000\n",
      "(Epoch 2 / 25) train acc: 0.520000; val_acc: 0.247000\n",
      "(Epoch 3 / 25) train acc: 0.618000; val_acc: 0.279000\n",
      "(Epoch 4 / 25) train acc: 0.650000; val_acc: 0.269000\n",
      "(Epoch 5 / 25) train acc: 0.778000; val_acc: 0.315000\n",
      "(Epoch 6 / 25) train acc: 0.780000; val_acc: 0.288000\n",
      "(Epoch 7 / 25) train acc: 0.812000; val_acc: 0.267000\n",
      "(Epoch 8 / 25) train acc: 0.866000; val_acc: 0.300000\n",
      "(Epoch 9 / 25) train acc: 0.870000; val_acc: 0.333000\n",
      "(Epoch 10 / 25) train acc: 0.932000; val_acc: 0.317000\n",
      "(Epoch 11 / 25) train acc: 0.940000; val_acc: 0.295000\n",
      "(Epoch 12 / 25) train acc: 0.960000; val_acc: 0.297000\n",
      "(Epoch 13 / 25) train acc: 0.954000; val_acc: 0.282000\n",
      "(Epoch 14 / 25) train acc: 0.958000; val_acc: 0.276000\n",
      "(Epoch 15 / 25) train acc: 0.978000; val_acc: 0.293000\n",
      "(Epoch 16 / 25) train acc: 0.972000; val_acc: 0.295000\n",
      "(Epoch 17 / 25) train acc: 0.972000; val_acc: 0.311000\n",
      "(Epoch 18 / 25) train acc: 0.962000; val_acc: 0.313000\n",
      "(Epoch 19 / 25) train acc: 0.982000; val_acc: 0.312000\n",
      "(Epoch 20 / 25) train acc: 0.990000; val_acc: 0.283000\n",
      "(Iteration 101 / 125) loss: 0.154802\n",
      "(Epoch 21 / 25) train acc: 0.982000; val_acc: 0.273000\n",
      "(Epoch 22 / 25) train acc: 0.992000; val_acc: 0.292000\n",
      "(Epoch 23 / 25) train acc: 0.982000; val_acc: 0.297000\n",
      "(Epoch 24 / 25) train acc: 0.990000; val_acc: 0.297000\n",
      "(Epoch 25 / 25) train acc: 0.988000; val_acc: 0.304000\n",
      "0.75\n",
      "(Iteration 1 / 125) loss: 11.589987\n",
      "(Epoch 0 / 25) train acc: 0.214000; val_acc: 0.168000\n",
      "(Epoch 1 / 25) train acc: 0.348000; val_acc: 0.211000\n",
      "(Epoch 2 / 25) train acc: 0.524000; val_acc: 0.263000\n",
      "(Epoch 3 / 25) train acc: 0.586000; val_acc: 0.321000\n",
      "(Epoch 4 / 25) train acc: 0.644000; val_acc: 0.285000\n",
      "(Epoch 5 / 25) train acc: 0.706000; val_acc: 0.266000\n",
      "(Epoch 6 / 25) train acc: 0.818000; val_acc: 0.313000\n",
      "(Epoch 7 / 25) train acc: 0.814000; val_acc: 0.286000\n",
      "(Epoch 8 / 25) train acc: 0.834000; val_acc: 0.286000\n",
      "(Epoch 9 / 25) train acc: 0.872000; val_acc: 0.291000\n",
      "(Epoch 10 / 25) train acc: 0.904000; val_acc: 0.305000\n",
      "(Epoch 11 / 25) train acc: 0.906000; val_acc: 0.295000\n",
      "(Epoch 12 / 25) train acc: 0.948000; val_acc: 0.303000\n",
      "(Epoch 13 / 25) train acc: 0.938000; val_acc: 0.316000\n",
      "(Epoch 14 / 25) train acc: 0.934000; val_acc: 0.287000\n",
      "(Epoch 15 / 25) train acc: 0.980000; val_acc: 0.306000\n",
      "(Epoch 16 / 25) train acc: 0.968000; val_acc: 0.298000\n",
      "(Epoch 17 / 25) train acc: 0.978000; val_acc: 0.307000\n",
      "(Epoch 18 / 25) train acc: 0.958000; val_acc: 0.288000\n",
      "(Epoch 19 / 25) train acc: 0.976000; val_acc: 0.303000\n",
      "(Epoch 20 / 25) train acc: 0.984000; val_acc: 0.304000\n",
      "(Iteration 101 / 125) loss: 0.840770\n",
      "(Epoch 21 / 25) train acc: 0.962000; val_acc: 0.286000\n",
      "(Epoch 22 / 25) train acc: 0.968000; val_acc: 0.293000\n",
      "(Epoch 23 / 25) train acc: 0.984000; val_acc: 0.306000\n",
      "(Epoch 24 / 25) train acc: 0.988000; val_acc: 0.302000\n",
      "(Epoch 25 / 25) train acc: 0.990000; val_acc: 0.302000\n",
      "0.5\n",
      "(Iteration 1 / 125) loss: 13.793884\n",
      "(Epoch 0 / 25) train acc: 0.238000; val_acc: 0.180000\n",
      "(Epoch 1 / 25) train acc: 0.370000; val_acc: 0.264000\n",
      "(Epoch 2 / 25) train acc: 0.464000; val_acc: 0.255000\n",
      "(Epoch 3 / 25) train acc: 0.600000; val_acc: 0.300000\n",
      "(Epoch 4 / 25) train acc: 0.656000; val_acc: 0.278000\n",
      "(Epoch 5 / 25) train acc: 0.716000; val_acc: 0.304000\n",
      "(Epoch 6 / 25) train acc: 0.770000; val_acc: 0.278000\n",
      "(Epoch 7 / 25) train acc: 0.784000; val_acc: 0.298000\n",
      "(Epoch 8 / 25) train acc: 0.812000; val_acc: 0.288000\n",
      "(Epoch 9 / 25) train acc: 0.804000; val_acc: 0.259000\n",
      "(Epoch 10 / 25) train acc: 0.838000; val_acc: 0.287000\n",
      "(Epoch 11 / 25) train acc: 0.918000; val_acc: 0.304000\n",
      "(Epoch 12 / 25) train acc: 0.880000; val_acc: 0.298000\n",
      "(Epoch 13 / 25) train acc: 0.942000; val_acc: 0.312000\n",
      "(Epoch 14 / 25) train acc: 0.928000; val_acc: 0.305000\n",
      "(Epoch 15 / 25) train acc: 0.950000; val_acc: 0.321000\n",
      "(Epoch 16 / 25) train acc: 0.948000; val_acc: 0.324000\n",
      "(Epoch 17 / 25) train acc: 0.940000; val_acc: 0.323000\n",
      "(Epoch 18 / 25) train acc: 0.948000; val_acc: 0.304000\n",
      "(Epoch 19 / 25) train acc: 0.968000; val_acc: 0.292000\n",
      "(Epoch 20 / 25) train acc: 0.974000; val_acc: 0.305000\n",
      "(Iteration 101 / 125) loss: 1.778800\n",
      "(Epoch 21 / 25) train acc: 0.968000; val_acc: 0.314000\n",
      "(Epoch 22 / 25) train acc: 0.956000; val_acc: 0.294000\n",
      "(Epoch 23 / 25) train acc: 0.968000; val_acc: 0.298000\n",
      "(Epoch 24 / 25) train acc: 0.984000; val_acc: 0.316000\n",
      "(Epoch 25 / 25) train acc: 0.940000; val_acc: 0.311000\n",
      "0.25\n",
      "(Iteration 1 / 125) loss: 18.316434\n",
      "(Epoch 0 / 25) train acc: 0.226000; val_acc: 0.196000\n",
      "(Epoch 1 / 25) train acc: 0.306000; val_acc: 0.210000\n",
      "(Epoch 2 / 25) train acc: 0.474000; val_acc: 0.287000\n",
      "(Epoch 3 / 25) train acc: 0.504000; val_acc: 0.291000\n",
      "(Epoch 4 / 25) train acc: 0.552000; val_acc: 0.263000\n",
      "(Epoch 5 / 25) train acc: 0.612000; val_acc: 0.313000\n",
      "(Epoch 6 / 25) train acc: 0.632000; val_acc: 0.302000\n",
      "(Epoch 7 / 25) train acc: 0.670000; val_acc: 0.292000\n",
      "(Epoch 8 / 25) train acc: 0.682000; val_acc: 0.306000\n",
      "(Epoch 9 / 25) train acc: 0.690000; val_acc: 0.308000\n",
      "(Epoch 10 / 25) train acc: 0.746000; val_acc: 0.315000\n",
      "(Epoch 11 / 25) train acc: 0.752000; val_acc: 0.306000\n",
      "(Epoch 12 / 25) train acc: 0.800000; val_acc: 0.329000\n",
      "(Epoch 13 / 25) train acc: 0.820000; val_acc: 0.310000\n",
      "(Epoch 14 / 25) train acc: 0.806000; val_acc: 0.307000\n",
      "(Epoch 15 / 25) train acc: 0.820000; val_acc: 0.290000\n",
      "(Epoch 16 / 25) train acc: 0.872000; val_acc: 0.297000\n",
      "(Epoch 17 / 25) train acc: 0.892000; val_acc: 0.316000\n",
      "(Epoch 18 / 25) train acc: 0.858000; val_acc: 0.333000\n",
      "(Epoch 19 / 25) train acc: 0.892000; val_acc: 0.302000\n",
      "(Epoch 20 / 25) train acc: 0.894000; val_acc: 0.295000\n",
      "(Iteration 101 / 125) loss: 5.453115\n",
      "(Epoch 21 / 25) train acc: 0.902000; val_acc: 0.283000\n",
      "(Epoch 22 / 25) train acc: 0.924000; val_acc: 0.294000\n",
      "(Epoch 23 / 25) train acc: 0.920000; val_acc: 0.305000\n",
      "(Epoch 24 / 25) train acc: 0.912000; val_acc: 0.309000\n",
      "(Epoch 25 / 25) train acc: 0.920000; val_acc: 0.309000\n"
     ]
    }
   ],
   "source": [
    "# Train two identical nets, one with dropout and one without\n",
    "np.random.seed(231)\n",
    "num_train = 500\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [1,0.9, 0.75, 0.5, 0.25]\n",
    "for dropout in dropout_choices:\n",
    "  model = FullyConnectedNet([500], dropout=dropout)\n",
    "  print(dropout)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=25, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True, print_every=100)\n",
    "  solver.train()\n",
    "  solvers[dropout] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a203f57da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot train and validation accuracies of the two models\n",
    "\n",
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "  solver = solvers[dropout]\n",
    "  train_accs.append(solver.train_acc_history[-1])\n",
    "  val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].train_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')          # ncol表示图例显示为一行两列形式\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].val_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2:\n",
    "Compare the validation and training accuracies with and without dropout -- what do your results suggest about dropout as a regularizer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "没有使用dropout的网络有明显的过拟合趋势，使用了dropout后虽然训练集的准确率有所下降，但验证集的准确率有所提升，这表示dropout起到了抑制过拟合的正则化作用，但dropout置零的节点过多会使模型的容量下降，从而导致模型的拟合能力降低，所以dropout置零的概率不能太大，需要设置适当的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "Suppose we are training a deep fully-connected network for image classification, with dropout after hidden layers (parameterized by keep probability p). How should we modify p, if at all, if we decide to decrease the size of the hidden layers (that is, the number of nodes in each layer)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "如果模型的过拟合比较严重，则使用较小的p，即只让少量的节点保持激活的状态，可以使用交叉验证的方法，使用多组dropout的值，先找到一个大致的概率值，然后在该值附近，进行精细的搜索，如果减小网络隐层的节点数，则模型的容量会下降，需要的正则化程度会降低，所以增大dropout的值来减轻正则化的程度"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
